{"product_id":"how-machine-learning-is-innovating-todays-world-a-concise-technical-guide-hardback-9781394214112","title":"How Machine Learning is Innovating Today's World; A Concise Technical Guide (Hardback) 9781394214112","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eHow Machine Learning is Innovating Today's World\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eA Concise Technical Guide\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eArindam Dey (Edited by), A Dey (Author), Sukanta Nayak (Edited by), Ranjan Kumar (Edited by), Sachi Nandan Mohanty (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394214112, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 July 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e25.4 x 17.8 x 2.9 cm, 1.202 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eProvides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMachine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. \u003ci\u003eHow Machine Learning is Innovating Today's World\u003c\/i\u003e is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries.\u003c\/p\u003e \u003cp\u003eIt provides a comprehensive understanding of the practical applications of ML techniques. The wide range of topics include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing\u003c\/li\u003e \u003cli\u003eexplores the evaluation of English language readability using ML models\u003c\/li\u003e \u003cli\u003ea detailed study of text analysis for information retrieval through natural language processing\u003c\/li\u003e \u003cli\u003ethe application of reinforcement learning approaches to supply chain management\u003c\/li\u003e \u003cli\u003ethe performance analysis of converting algorithms to source code using natural language processing in Java\u003c\/li\u003e \u003cli\u003epresents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques\u003c\/li\u003e \u003cli\u003ea comparative study of different techniques of text-to-SQL query conversion\u003c\/li\u003e \u003cli\u003ethe classification of livestock diseases using ML algorithms\u003c\/li\u003e \u003cli\u003eML in image enhancement techniques\u003c\/li\u003e \u003cli\u003ethe efficient leader selection for inter-cluster flying ad-hoc networks\u003c\/li\u003e \u003cli\u003ea comprehensive survey of applications powered by GPT-3 and DALL-E\u003c\/li\u003e \u003cli\u003erecommender systems' domain of application\u003c\/li\u003e \u003cli\u003ereviews mood detection, emoji generation, and classification using tokenization and CNN\u003c\/li\u003e \u003cli\u003evariations of the exam scheduling problem using graph coloring\u003c\/li\u003e \u003cli\u003ethe intersection of software engineering and machine learning applications\u003c\/li\u003e \u003cli\u003eexplores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments\u003c\/li\u003e \u003cli\u003eML applications in healthcare, in battery management systems, and the rise of AI-generated news videos\u003c\/li\u003e \u003cli\u003ehow to enhance resource management in precision farming through AI-based irrigation optimization.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how machine learning and artificial intelligence can benefit their work.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Natural Language Processing (NLP) Applications 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 A Comprehensive Analysis of Various Tokenization Techniques and Sequence-to-Sequence Model in Natural Language Processing 3\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Ashutosh M. Kulkarni, Gitanjali Bhimrao Yadav, R. Kumar and Aparna R. Sawant\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 A Review on Text Analysis Using NLP 13\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Text Generation \u0026amp; Classification in NLP: A Review 25\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Dattatray Raghunath Kale, Jagannath Nalavade, R. Kumar and Hanmant D. Magar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Book Genre Prediction Using NLP: A Review 37\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Preeti Bailke, Ashutosh M. Kulkarni, R. Kumar and Ajit B. Patil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Mood Detection Using Tokenization: A Review 47\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Preeti A. Bailke, Lokesh Sheshrao Khedekar, R. Kumar and Varsha R. Dange\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Converting Pseudo Code to Code: A Review 57\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Preeti A. Bailke, Anita Bapu Dombale, Varsha R. Dange and Ashutosh M. Kulkarni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Machine Learning Applications in Specific Domains 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7 Evaluating the Readability of English Language Using Machine Learning Models 71\u003cbr\u003e\u003ci\u003eShiplu Das, Abhishikta Bhattacharjee, Gargi Chakraborty and Debarun Joardar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Machine Learning in Maximizing Cotton Yield with Special Reference to Fertilizer Selection 89\u003cbr\u003e\u003ci\u003eG. Hannah Grace and Nivetha Martin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Machine Learning Approaches to Catalysis 101\u003cbr\u003e\u003ci\u003eSachidananda Nayak and Selvakumar Karuthapandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Classification of Livestock Diseases Using Machine Learning Algorithms 127\u003cbr\u003e\u003ci\u003eG. Hannah Grace, Nivetha Martin, I. Pradeepa and N. Angel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Image Enhancement Techniques to Modify an Image with Machine Learning Application 139\u003cbr\u003e\u003ci\u003eShiplu Das, Sohini Sen, Debarun Joardar and Gargi Chakraborty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Software Engineering in Machine Learning Applications: A Comprehensive Study 159\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Komal Sunil Munde, Amol A. Bhosle, Aparna R. Sawant and Ashutosh M. Kulkarni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Machine Learning Applications in Battery Management System 173\u003cbr\u003e\u003ci\u003ePonnaganti Chandana and Ameet Chavan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 ML Applications in Healthcare 201\u003cbr\u003e\u003ci\u003eFarooq Shaik, Rajesh Yelchurri, Noman Aasif Gudur and Jatindra Kumar Dash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Enhancing Resource Management in Precision Farming through AI-Based Irrigation Optimization 221\u003cbr\u003e\u003ci\u003eSalina Adinarayana, Matha Govinda Raju, Durga Prasad Srirangam, Devee Siva Prasad, Munaganuri Ravi Kumar and Sai babu veesam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 An In-Depth Review on Machine Learning Infusion in an Agricultural Production System 253\u003cbr\u003e\u003ci\u003eSarthak Dash, Sugyanta Priyadarshini and Sukanya Priyadarshini\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Artificial Intelligence and Optimization Techniques 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17 Reinforcement Learning Approach in Supply Chain Management: A Review 273\u003cbr\u003e\u003ci\u003eRajkanwar Singh, Pratik Mandal and Sukanta Nayak\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Alternate Approach to Solve Differential Equations Using Artificial Neural Network with Optimization Technique 303\u003cbr\u003e\u003ci\u003eRamanan R., Sukanta Nayak and Arun Kumar Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19 GPT-3- and DALL-E-Powered Applications: A Complete Survey 329\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Chaitanya B. Pednekar, Priya Anup Khune, Vinay Sudhir Prabhavalkar and Varsha R. Dange\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20 New Variation of Exam Scheduling Problem Using Graph Coloring 343\u003cbr\u003e\u003ci\u003eAngshu Kumar Sinha, Soumyadip Laha, Debarghya Adhikari, Anjan Koner and Neha Deora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Emerging Topics in Machine Learning 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21 A Comparative Study of Different Techniques of Text-to-SQL Query Converter 355\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Preeti A. Bailke, Vikas Janu Nandeshwar, R. Kumar and Varsha R. Dange\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22 Trust-Based Leader Election in Flying Ad-Hoc Network 367\u003cbr\u003e\u003ci\u003eJoydeep Kundu, Sahabul Alam and Sukanta Oraw\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23 A Survey on Domain of Application of Recommender System 375\u003cbr\u003e\u003ci\u003eSudipto Dhar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24 New Approach on M\/M\/c\/K Queueing Models via Single Valued Linguistic Neutrosophic Numbers and Perceptionization Using a Non-Linear Programming Technique 383\u003cbr\u003e\u003ci\u003eAntony Crispin Sweety C. and Vennila B.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25 The Rise of AI-Generated News Videos: A Detailed Review 423\u003cbr\u003e\u003ci\u003eKuldeep Vayadande, Mustansir Bohri, Mohit Chawala, Ashutosh M. Kulkarni and Asif Mursal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 449\u003c\/p\u003e \u003cp\u003eIndex 453\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433207820568,"sku":"9781394214112","price":134.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394214112.jpg?v=1784851839","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/how-machine-learning-is-innovating-todays-world-a-concise-technical-guide-hardback-9781394214112","provider":"Freshly Printed Books","version":"1.0","type":"link"}